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Oblique projection beamforming for RFI mitigation in radio astronomy

Radio astronomical observations are increasingly corrupted by radio frequency interference (RFI). Phased antenna array radio telescopes allow the recovering of spatial information of RFI and cosmic sources. Using this information, spatial signal processing techniques can limit the impact of the incoming interferences. In this article, we present an RFI mitigation technique, based on an oblique projector.

Foetal PQRST extraction from ECG recordings using cyclostationarity-based source separation method

This work proposes a novel foetal electrocardiogram (FECG) extraction approach based on the cyclostationary properties of the signal of interest. The problem of FECG extraction can easily fit in a blind source separation (BSS) framework; taking into account specific statistical nature of the signal, that one wants to extract, leads to an algorithm able to estimate the FECG contribution to ECG recordings where the maternal ECG is predominant. We show that the proposed procedure provides estimates of the FECGs PQRST complexes without incorporating any prior knowledge concerning PQRST features. Discussions about foetal heart rate variability (HRV) estimation and future works conclude this paper.

Extraction of a cyclostationary source using a new cost function without pre-whitening

The proposed method aims to extract a cyclostationary source, whose cyclic frequency is a priori known, from a set of additive mixtures. The other sources may be either stationary or cyclostationary as long as their cyclic frequencies are different from that of the source to be extracted. The method does not require pre-whitening and consists in minimizing a criterion based on stationary and cyclostationary second order statistics of the observations; this method is labeled as Second Order Cyclostationary Statistics Optimization Criterion (SOC^2). The relevance of this criterion is proven theoretically in the general case of N sources by P sensors, with P>=N. Other properties of the algorithm such as its accuracy and its robustness against additive noise or strong interferences are studied through a set of simulations.

Speed Transform, a New Time-Varying Frequency Analysis Technique

Due to the periodical motions of most machinery in steady state operation, many diagnosis techniques are based on frequency analysis. This is often performed through Fourier transform. Some extensions of these techniques to the more general case of non stationary operation have been proposed. They are based on signal processing advances such as time–frequency representations and adaptive filtering. The technique proposed in this paper is based on the observation that, when under non stationary operation, the vibrations of a machine are still tightly related to the speed variations. It is thus suggested to decompose the vibration signal over a set of time-varying frequency sine waves synchronized with the speed variations, instead of fixed frequency sine waves. This set of time-varying frequency sine waves is shown to be an orthonormal basis of the subspace it spans in the case of linear frequency variations. An insight to the improvement such decomposition can provide for spectral analysis, cyclostationary analysis and time–frequency representation is given. Some application examples are presented over both simulated signals and real-life signals.

A new source extraction algorithm for cyclostationary sources

Cyclostationary signals can be met in various domains, such as telecomunications and vibration analysis. Cyclostationarity allows to model repetitive signals and hidden periodicities such as those induced by modulation for communications and by rotating machines for vibrations. In some cases, the fundamental frequency of these repetitive phenomena can be known. The algorithm that we propose aims at extracting one cyclostationary source, whose cyclic frequency is a priori known, from a set of observations. We propose a new criterion based on second order statistics of the measures which is easy to estimate and leads to extraction with very good accuracy.

A New Approach to Tune the Vold-Kalman Estimator for Order Tracking

In the purpose to diagnose rotating machines using vibration signal, engineers use order tracking method to process non-stationary signals . We deal here with order tracking when the vibration signal is represented in a state space model. Such a methodology leads to the Kalman estimator that requires knowledge about the noise statistics affecting the state and the measurement equation. These noise statistics are usually unknown and need to be estimated from operating data for the use of the Kalman estimation algorithm. Several methods to tune these parameters have been developed for time-invariant model. In this paper, we introduce a technique to estimate the noise covariances for a linear time-variant system using the innovation process. The efficiency of this new approach is evaluated using a synthetic non-stationary vibration signal. The advantage of this approach is that it converges quickly and provides a small estimation error compared to those used for the linear time-invariant model.

Estimation of Cyclic Cumulants of Machinery Vibration Signals in Non-stationary Operation

Cyclic statistics have been proved to be a powerful tool for the study of rotating machinery vibration signals. Indeed, such signals usually exhibit cyclostationary features related to the shaft speed and to the geometry of the components. Cyclostationarity can be studied at order one (periodic deterministic components) or order 2 and more. Cyclic statistics at order N comprise a pure Nth order cyclostationary part and a contribution from orders 1 to N − 1. It may be interesting to study pure cyclostationarity at order N, i.e. to remove the influence of smaller orders. This can be done by computing cyclic cumulants instead of cyclic moments. In order to compute 2nd order cumulants of the vibration signal, one must remove from the signal the 1st order cyclostationary components, that is to say the deterministic periodic components. Some classical approaches have been proposed, based on synchronized averaging or Fourier transform. But some limitations appear when the vibration signal comprises components tied to different rotation frequencies (for instance in the case of gears) or under variable speed. The method that we propose in order to extract these periodic components is based on a biquad filter bank. Biquad filters have been extensively used in audio processing and allow building band-pass or notch filter banks at low computational cost. We show how such filters can be used to remove the 1st order cyclic components from the signal. An extension to variable speed operation is proposed by having the filters central frequency follow the variations of the rotation frequency. The technique is applied to simulated signals as well as real life signals.

H ∞ estimator for gearbox diagnosis in variable speed conditions

It is well known that a faulty gearbox vibration signal exhibits an amplitude modulation (AM) as well as a phase modulation (PM). These modulation carry out a lot of useful information about health condition. This paper presents two approaches for modeling amplitude and phase modulation in gearbox vibration signal. These last are used to describe the vibration signal by a state space model. Then, the H∞ estimator is designed to estimate the modulation appearing in the vibration signal. This estimator is obtained by minimizing the worst possible amplification effects of disturbances (measurement and modeling noises) on the estimation error. Such an estimator does not require any assumption on the statistic properties of the noises. Since additive noises in gearbox vibration signal are non Gaussian and non white, this estimator is more suitable in practical gearbox diagnosis. To evaluate the performance of the two approaches, we use a synthetic and an experimental gearbox vibration signal.

Extraction of foetal contribution to ECG recordings using cyclostationarity-based source separation method

In this paper we propose a cyclostationary approach to the problem of the foetal electrocardiogram (FECG) extraction from a set of cutaneous potential recordings of an expectant mother. We adopted a semi-blind source separation (BSS) method for which the only necessary prior knowledge is that of the fundamental cyclic frequency of the cyclostationary process to be estimated. Using this technique, the estimated cyclostationary FECG source of interest is found to be free from any interferences with the mothers ECG (MECG) signal. Experimental results and perspectives for future research conclude this paper.